|Publication number||US8065184 B2|
|Application number||US 11/321,076|
|Publication date||Nov 22, 2011|
|Priority date||Dec 30, 2005|
|Also published as||CA2635884A1, CN101390119A, EP1966749A2, EP1966749A4, US20070156514, WO2007079403A2, WO2007079403A3|
|Publication number||11321076, 321076, US 8065184 B2, US 8065184B2, US-B2-8065184, US8065184 B2, US8065184B2|
|Inventors||Daniel Wright, Daryl Pregibon, Diane Tang|
|Original Assignee||Google Inc.|
|Export Citation||BiBTeX, EndNote, RefMan|
|Patent Citations (78), Non-Patent Citations (16), Referenced by (20), Classifications (16), Legal Events (3)|
|External Links: USPTO, USPTO Assignment, Espacenet|
1. Field of the Invention
Implementations described herein relate generally to on-line advertisements and, more particularly, to estimating qualities of on-line advertisements using observed user behavior.
2. Description of Related Art
On-line advertising systems host advertisements that may advertise various services and/or products. Such advertisements may be presented to users accessing documents hosted by the advertising system. An advertisement may include a “creative,” which includes text, graphics and/or images associated with the advertised service and/or product. The advertisement may further include a link to an ad “landing document” which contains further details about the advertised service(s) and/or product(s). When a particular creative appears to be of interest to a user, the user may select (or click) the creative, and the associated link causes a user's web browser to visit the “landing document” associated with the creative and link. This selection of an advertising creative and associated link by a user is referred to hereinafter as a “click.”
On-line advertising systems often track ad clicks for billing and other purposes. One non-billing purpose for tracking ad clicks is to attempt to ascertain advertisement quality. The click through rate (CTR) is a measure used to determine advertisement quality. CTR represents the fraction of times a given ad gets “clicked” on when an advertisement creative is presented to users. The CTR of an advertisement, however, is an imperfect measure of advertisement quality since it focuses on the advertisement creative rather than the object of that advertisement, which is the landing document. A user needs to click on an advertisement in order to determine if an advertisement is good or bad and, therefore, the occurrence/non-occurrence of a click is insufficient to determine the quality of an advertisement. Some advertisements receive many clicks because they have a good creative, but the landing document is completely unsatisfying, or irrelevant, to the user. Other advertisements receive very few clicks (e.g., due to the advertisement creative being poor), but every click leads to a satisfied user. Existing determinations of CTR associated with on-line advertisements, thus, provide imperfect measures of advertisement quality.
According to one aspect, a method may include obtaining ratings associated with a first set of advertisements hosted by one or more servers, where the ratings indicate a quality of the first set of advertisements. The method may further include observing multiple different first user actions associated with user selection of advertisements of the first set of advertisements and deriving a statistical model using the observed first user actions and the obtained ratings. The method may also include observing second user actions associated with user selection of a second advertisement hosted by the one or more servers and using the statistical model and the second user actions to estimate a quality of the second advertisement.
According to another aspect, a method may include observing first user behavior associated with user selection of a first advertisement hosted by a server, where the first observed user behavior includes user behavior other than, or in addition to, a click through rate (CTR). The method may further include estimating a quality of the first advertisement based on the observed first user behavior.
According to a further aspect, a method may include observing multiple measurable user actions associated with user selections of first advertisements hosted by one or more servers and correlating known qualities associated with the first advertisements with certain ones of the multiple measurable user actions. The method may further include observing the presence of the certain ones of the multiple measurable user actions associated with user selection of a second advertisement and estimating a quality of the second advertisement based on the presence of the certain ones of the multiple measurable user actions.
According to an additional aspect, a method may include logging user actions associated with user selection of a group of advertisements, where the group of advertisements is associated with quality ratings and where the user actions include user actions other than, or in addition to, a click through rate (CTR). The method may further include generating a statistical model using the logged user actions and the quality ratings and estimating qualities of advertisements not included in the group of advertisements using the statistical model.
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate one or more embodiments of the invention and, together with the description, explain the invention. In the drawings,
The following detailed description of the invention refers to the accompanying drawings. The same reference numbers in different drawings may identify the same or similar elements. Also, the following detailed description does not limit the invention.
Systems and methods consistent with aspects of the invention may use multiple observations of user behavior (e.g., real-time observations or observations from recorded user logs) associated with user selection of on-line advertisements to more accurately estimate advertisement quality as compared to conventional determinations of quality based solely on CTR. Quality ratings associated with known rated advertisements, and corresponding measured observed user behavior associated with selections (e.g., “clicks”) of those known rated advertisements, may be used to construct a statistical model. The statistical model may subsequently be used to estimate qualities associated with unrated advertisements based on observed user behavior associated with selections of the unrated advertisements. The statistical model may, thus, given a set of observations of user behavior associated with the selection of an advertisement, estimate the likelihood of the user being satisfied with the selected advertisement.
In response to receipt of an advertisement 100, the receiving user may, based on the “creative” displayed on the advertisement, select 110 the advertisement (e.g., “click” on the displayed advertisement using a mouse). After ad selection 110, an ad landing document 115 may be provided to the selecting user by a server hosting the advertisement using a link embedded in ad 100. The ad landing document 115 may provide details of the product(s) and/or service(s) advertised in the corresponding advertisement 100.
Before, during and/or after each ad selection 110 by a user, session features 125 associated with each ad selection 110 during a “session” may be measured in real-time or logged in memory. A session may include a group of user actions that occur without a break of longer than a specified period of time (e.g., a group of user actions that occur without a break of longer than three hours).
The measured session features 125 can include any type of observed user behavior or actions. For example, session features 125 may include a duration of the ad selection 110 (e.g., a duration of the “click” upon the ad 100), the number of selections of other advertisements before and/or after a given ad selection, the number of selections of search results before and/or after a given ad selection, the number of selections on other types of results (e.g., images, news, products, etc.) before and/or after a given ad selection, a number of document views (e.g., page views) before and/or after a given ad selection (e.g., page views of search results before and/or after the ad selection), the number of search queries before and/or after a given ad selection, the number of queries associated with a user session that show advertisements, the number of repeat selections on a same given advertisement, or an indication of whether a given ad selection was the last selection in a session, the last ad selection in a session, the last selection for a given search query, or the last ad selection for a given search query. Other types of observed user behavior associated with ad selection, not described above, may be used consistent with aspects of the invention.
Using the measured session features 125 and ad ratings data 120, associated with each ad selection 110 of a corresponding rated advertisement 100, a statistical model 130 may be constructed (as further described below). The statistical model may include a probability model derived using existing statistical techniques. Such techniques may include, for example, logistic regression, regression trees, boosted stumps, or other existing statistical modeling techniques. Statistical model 130 may determine the likelihood that a given advertisement 100 is good or bad given measured session features associated with a user selection of the advertisement 100 (e.g., P(good ad|ad selection)=f(session features)).
Subsequent to construction of statistical model 130, a quality of an unrated advertisement selected by a user may be estimated. An unrated ad 135, associated with a document 140, may be hosted by a server in a network and may be provided to an accessing user. Session features 155 associated with user selection 145 of unrated ad 135 may be measured, and the measurements may be provided as inputs into statistical model 130. Statistical model 130 may determine a likelihood that the unrated ad 135 is a good ad, given the measured session features, and may generate an estimated quality 160 of the unrated ad 135. The estimated quality 160 of the unrated ad 135 may be used for various purposes, such as, for example, ranking multiple advertisements among one another, determining which advertisements to present or to promote (e.g., differentially alter ad placement on a given document), etc.
Clients 210 may include client entities. An entity may be defined as a device, such as a personal computer, a wireless telephone, a personal digital assistant (PDA), a lap top, or another type of computation or communication device, a thread or process running on one of these devices, and/or an object executable by one of these devices. One or more users may be associated with each client 210. Servers 220 and 230 may include server entities that access, fetch, aggregate, process, search, and/or maintain documents in a manner consistent with the principles of the invention. Clients 210 and servers 220 and 230 may connect to network 240 via wired, wireless, and/or optical connections.
In an implementation consistent with the principles of the invention, server 220 may include a search engine system 225 usable by users at clients 210. Server 220 may implement a data aggregation service by crawling a corpus of documents (e.g., web documents), indexing the documents, and storing information associated with the documents in a repository of documents. The data aggregation service may be implemented in other ways, such as by agreement with the operator(s) of data server(s) 230 to distribute their hosted documents via the data aggregation service. In some implementations, server 220 may host advertisements (e.g., creatives, ad landing documents) that can be provided to users at clients 210. Search engine system 225 may execute a query, received from a user at a client 210, on the corpus of documents stored in the repository of documents, and may provide a set of search results to the user that are relevant to the executed query. In addition to the set of search results, server 220 may provide one or more advertising creatives, associated with results of the executed search, to the user at client 210.
Server(s) 230 may store or maintain documents that may be crawled by server 220. Such documents may include data related to published news stories, products, images, user groups, geographic areas, or any other type of data. For example, server(s) 230 may store or maintain news stories from any type of news source, such as, for example, the Washington Post, the New York Times, Time magazine, or Newsweek. As another example, server(s) 230 may store or maintain data related to specific products, such as product data provided by one or more product manufacturers. As yet another example, server(s) 230 may store or maintain data related to other types of web documents, such as pages of web sites. Server(s) 230 may further host advertisements, such as ad creatives and ad landing documents.
Network 240 may include one or more networks of any type, including a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), a telephone network, such as the Public Switched Telephone Network (PSTN) or a Public Land Mobile Network (PLMN), an intranet, the Internet, a memory device, or a combination of networks. The PLMN(s) may further include a packet-switched sub-network, such as, for example, General Packet Radio Service (GPRS), Cellular Digital Packet Data (CDPD), or Mobile IP sub-network.
While servers 220-230 are shown as separate entities, it may be possible for one of servers 220-230 to perform one or more of the functions of the other one of servers 220-230. For example, it may be possible that servers 220 and 230 are implemented as a single server. It may also be possible for a single one of servers 220 and 230 to be implemented as two or more separate (and possibly distributed) devices.
Processor 320 may include a conventional processor, microprocessor, or processing logic that may interpret and execute instructions. Main memory 330 may include a random access memory (RAM) or another type of dynamic storage device that may store information and instructions for execution by processor 320. ROM 340 may include a conventional ROM device or another type of static storage device that may store static information and instructions for use by processor 320. Storage device 350 may include a magnetic and/or optical recording medium and its corresponding drive.
Input device 360 may include a conventional mechanism that permits an operator to input information to the client/server entity, such as a keyboard, a mouse, a pen, voice recognition and/or biometric mechanisms, etc. Output device 370 may include a conventional mechanism that outputs information to the operator, including a display, a printer, a speaker, etc. Communication interface 380 may include any transceiver-like mechanism that enables the client/server entity to communicate with other devices and/or systems. For example, communication interface 380 may include mechanisms for communicating with another device or system via a network, such as network 240.
The client/server entity, consistent with the principles of the invention, may perform certain operations or processes, as will be described in detail below. The client/server entity may perform these operations in response to processor 320 executing software instructions contained in a computer-readable medium, such as memory 330. A computer-readable medium may be defined as a physical or logical memory device and/or carrier wave.
The software instructions may be read into memory 330 from another computer-readable medium, such as data storage device 350, or from another device via communication interface 380. The software instructions contained in memory 330 may cause processor 320 to perform operations or processes that will be described later. Alternatively, hardwired circuitry may be used in place of or in combination with software instructions to implement processes consistent with the principles of the invention. Thus, implementations consistent with the principles of the invention are not limited to any specific combination of hardware circuitry and software.
The exemplary process may begin with obtaining ratings data associated with rated advertisements (block 400). The ratings data may include human generated data that rates the quality of each of the rated ads (e.g., relative to the query issued). Session features associated with each selection of a rated advertisement may then be obtained (block 410). The session features may be obtained in real-time by observing actual user behavior during a given user session, that occurred before, during and after the presentation of each ad impression to a user, or may be obtained from recorded logs of session features (i.e., user behavior and actions) that were stored in a database before, during and/or after the presentation of each ad impression to a user. The obtained session features 125 can include any type of observed user behavior. Each of the session features 125 may correspond to an indirect measurement of user satisfaction with a given advertisement. Certain ones of the session features 125 may be factors in determining how different users have different values for other ones of the session features 125 (e.g., users with dial-up connections may have longer ad selection durations than users who have high speed Internet connections).
Session features 125 may include, but are not limited to, a duration of an ad selection (e.g., a duration of the “click” upon the advertisement), a number of selections of other advertisements before and/or after a given ad selection, a number of selections of search results before and/or after a given ad selection, a number of selections of other results before and/or after a given ad selection, a number of document views (e.g., page views) before and/or after a given ad selection, a number of search queries before and/or after a given ad selection, a number of search queries associated with a user session that show advertisements, a number of repeat selections on a same given advertisement, or an indication of whether a given ad selection was the last selection in a session, the last ad selection in a session, a last selection for a given search query, or the last ad selection for a given search query.
Other types of user behavior, not shown in
y=c 0 +c 1 *x 1 +c 2 *x 2+ . . . Eqn. (1)
and finds the values of c0, c1, c2, etc. (c0 is called the “intercept” or “constant term”). In the context of the present invention, each predictor variable x1, x2, x3, etc. corresponds to a different session feature measured during ad selection. Logistic regression is a variation of ordinary regression, useful when the observed outcome is restricted to two values, which usually represent the occurrence or non-occurrence of some outcome event, (usually coded as 1 or 0, respectively), such as a good advertisement or a bad advertisement in the context of the present invention.
Logistic regression produces a formula that predicts the probability of the occurrence as a function of the independent predictor variables. Logistic regression fits a special s-shaped curve by taking the linear regression (Eqn. (1) above), which could produce any y-value between minus infinity and plus infinity, and transforming it with the function:
P=exp(y)/(1+exp(y)) Eqn. (2)
which produces P-values between 0 (as y approaches minus infinity) and 1 (as y approaches plus infinity). Substituting Eqn. (1) into Eqn. (2), the probability of a good advertisement, thus, becomes the following:
where cg0 is the constant of the equation, and cgn is the coefficient of the session feature predictor variable xn. The probability of a bad advertisement may, similarly, be determined by the following:
where cb0 is the constant of the equation, and cbn is the coefficient of the session feature predictor variables xn.
A fit of the statistical model may be tested to determine which session features are correlated with good or bad quality advertisements. If a logistic regression technique is used to determine the statistical model, the goal of logistic regression is to correctly predict the outcome for individual cases using the most parsimonious model. To accomplish this goal, a model is created that includes all predictor variables (e.g., session features) that are useful in predicting the outcome of the dependent y variable. To construct the statistical model, logistic regression can test the fit of the model after each coefficient (cn) is added or deleted, called stepwise regression. For example, backward stepwise regression may be used, where model construction begins with a full or saturated model and predictor variables, and their coefficients, are eliminated from the model in an iterative process. The fit of the model is tested after the elimination of each variable to ensure that the model still adequately fits the data. When no more predictor variables can be eliminated from the model, the model construction has been completed. The predictor variables that are left in the model, each corresponding to a measured session feature, identify the session features that are correlated with good or bad advertisements. Logistic regression, thus, can provide knowledge of the relationships and strengths among the different predictor variables. The process by which coefficients, and their corresponding predictor variables, are tested for significance for inclusion or elimination from the model may involve several different known techniques. Such techniques may include the Wald test, the Likelihood-Ratio test, or the Hosmer-Lemshow Goodness of Fit test. These coefficient testing techniques are known in the art and are not further described here. In other implementations, existing techniques of cross validation and independent training may be used instead of techniques of classical estimation and testing of regression coefficients, as described above.
Other existing statistical techniques, instead of, or in addition to logistic regression, may be used to derive a statistical model consistent with principles of the invention. For example, a “stumps” model, using “boosting” techniques may be used to derive the statistical model. As one skilled in the art will recognize, “boosting” is a machine learning technique for building a statistical model by successively improving an otherwise weak statistical model. The basic idea is to repeatedly apply the same algorithm to an entire training data set, but differentially weight the training data at each stage. The weights are such that cases that are well-fit by the model through stage k receive relatively small weights at stage k+1, while cases that are ill-fit by the model through stage k receive relatively large weights at stage k+1.
Stumps are a weak statistical model that can be applied at each stage. A stump is a 2-leaf classification tree consisting of a root node and a binary rule that splits the cases into two mutually exclusive subsets (i.e., the leaf nodes). A rule could take the form “ClickDuration<120 sec” and all cases with ClickDuration satisfying the rule go into one leaf node and those not satisfying the rule go into the other leaf node. Another rule could take the form “AdSelection was the last ad selection” and all cases with AdSelection satisfying the rule go into one leaf node and those not satisfying the rule go into the other leaf node.
Various algorithms can be used to fit the “boosted stump” model including, for example, gradient-based methods. Such algorithms may proceed as follows: given a set of weights, among all possible binary decision rules derived from session features that partition the cases into two leaves, choose that one which minimizes the (weighted) loss function associated with the algorithm. Some examples of loss functions are “Bernoulli loss” corresponding to a maximum likelihood method, and “exponential loss” corresponding to the well-known ADABoost method. After choosing the best binary decision rule at this stage, the weights may be recomputed and the process may be repeated whereby the best binary rule is chosen which minimizes the new (weighted) loss function. This process may be repeated many times (e.g., several hundred to several thousand) and a resampling technique (such as cross-validation) may be used to define a stopping rule in order to prevent over-fitting.
Boosted stumps have been shown to approximate additive logistic regression models whereby each feature makes an additive nonlinear contribution (on the logistic scale) to the fitted model. The sequence of stumps define the relationship between session features and the probability that an ad is rated “good”. The sequence can be expressed by the statistical model:
where Bk(x)=1 if session feature x satisfies the kth binary rule, or Bk(x)=0 if session feature x does not satisfy the kth binary rule. The coefficients ck, k=1, . . . , are a by-product of the algorithm and relate to the odds of a good ad at the kth binary rule. In practice, given session feature x, each binary rule can be evaluated and the corresponding coefficients accumulated to get the predicted probability of a good ad.
Though logistic regression and boosted stumps have been described above as exemplary techniques for constructing a statistical model, one skilled in the art will recognize that other existing statistical techniques, such as, for example, regression trees may be used to derive the statistical model consistent with principles of the invention.
The exemplary process may begin with obtaining session features associated with the selection of unrated ads (block 1300). The session features may be measured in real-time during user ad selection or may be obtained from logs of user behavior associated with ad selection. The unrated ads may not have any human generated quality ratings associated with them, such as the ad ratings used in
A quality of the selected ad may then be estimated based on the determined score (block 1320). For example, ranges of quality scores may be used to estimate a quality of the selected ad, such as, for example, quality scores between values 0 and X may represent a low quality score, and quality score values between X and X+Y may represent a high quality score. Other methods may be used, consistent with principles of the invention, to estimate a quality of the selected ad based on the determined quality score.
The quality scores, determined in block 1310, may be aggregated, for multiple ad selections by many users, in a data structure as described in co-pending U.S. application Ser. No. 11/321,046, entitled “Predicting Ad Quality,” filed on a same date herewith, and incorporated by reference herein in its entirety. Quality predictions, resulting from the aggregated quality scores, may subsequently be used, for example, to filter, rank and/or promote advertisements as described in co-pending U.S. application Ser. No. 11/321,064, entitled “Using Estimated Ad Qualities for Ad Filtering, Ranking and Promotion,” filed on a same date herewith, and incorporated by reference herein in its entirety.
The foregoing description of preferred embodiments of the present invention provides illustration and description, but is not intended to be exhaustive or to limit the invention to the precise form disclosed. Modifications and variations are possible in light of the above teachings, or may be acquired from practice of the invention. For example, while series of acts have been described with regard to
In addition to the session features described above, conversion tracking may optionally be used in some implementations to derive a direct calibration between predictive values and user satisfaction. A conversion occurs when a selection of an advertisement leads directly to user behavior (e.g., a user purchase) that the advertiser deems valuable. An advertiser, or a service that hosts the advertisement for the advertiser, may track whether a conversion occurs for each ad selection. For example, if a user selects an advertiser's ad, and then makes an on-line purchase of a product shown on the ad landing document that is provided to the user in response to selection of the ad, then the advertiser, or service that hosts the ad, may note the conversion for that ad selection. The conversion tracking data may be associated with the identified ad selections. A statistical technique, such as, for example, logistic regression, regression trees, boosted stumps, etc., may be used to derive a direct calibration between predictive values and user happiness as measured by conversion.
It will be apparent to one of ordinary skill in the art that aspects of the invention, as described above, may be implemented in many different forms of software, firmware, and hardware in the implementations illustrated in the figures. The actual software code or specialized control hardware used to implement aspects consistent with the principles of the invention is not limiting of the invention. Thus, the operation and behavior of the aspects have been described without reference to the specific software code, it being understood that one of ordinary skill in the art would be able to design software and control hardware to implement the aspects based on the description herein.
No element, act, or instruction used in the present application should be construed as critical or essential to the invention unless explicitly described as such. Also, as used herein, the article “a” is intended to include one or more items. Where only one item is intended, the term “one” or similar language is used. Further, the phrase “based on” is intended to mean “based, at least in part, on” unless explicitly stated otherwise.
|Cited Patent||Filing date||Publication date||Applicant||Title|
|US5794210||Dec 11, 1995||Aug 11, 1998||Cybergold, Inc.||Attention brokerage|
|US5848396||Apr 26, 1996||Dec 8, 1998||Freedom Of Information, Inc.||Method and apparatus for determining behavioral profile of a computer user|
|US5918014||Dec 26, 1996||Jun 29, 1999||Athenium, L.L.C.||Automated collaborative filtering in world wide web advertising|
|US5937390||Jun 28, 1996||Aug 10, 1999||Fujitsu Limited||On-line advertising system and its method|
|US5937392||Jul 28, 1997||Aug 10, 1999||Switchboard Incorporated||Banner advertising display system and method with frequency of advertisement control|
|US5948061||Oct 29, 1996||Sep 7, 1999||Double Click, Inc.||Method of delivery, targeting, and measuring advertising over networks|
|US6006197||Apr 20, 1998||Dec 21, 1999||Straightup Software, Inc.||System and method for assessing effectiveness of internet marketing campaign|
|US6006222||Aug 1, 1997||Dec 21, 1999||Culliss; Gary||Method for organizing information|
|US6009409||Apr 2, 1997||Dec 28, 1999||Lucent Technologies, Inc.||System and method for scheduling and controlling delivery of advertising in a communications network|
|US6014665||Oct 29, 1997||Jan 11, 2000||Culliss; Gary||Method for organizing information|
|US6078901||Apr 3, 1997||Jun 20, 2000||Ching; Hugh||Quantitative supply and demand model based on infinite spreadsheet|
|US6078916||Mar 12, 1998||Jun 20, 2000||Culliss; Gary||Method for organizing information|
|US6097566||Apr 1, 1998||Aug 1, 2000||Quantum Corporation||Multi-drive, multi-magazine mass storage and retrieval unit for tape cartridges|
|US6182068||Mar 1, 1999||Jan 30, 2001||Ask Jeeves, Inc.||Personalized search methods|
|US6260064||Jan 8, 1999||Jul 10, 2001||Paul J. Kurzrok||Web site with automatic rating system|
|US6285999||Jan 9, 1998||Sep 4, 2001||The Board Of Trustees Of The Leland Stanford Junior University||Method for node ranking in a linked database|
|US6334110||Mar 10, 1999||Dec 25, 2001||Ncr Corporation||System and method for analyzing customer transactions and interactions|
|US6338066||Sep 25, 1998||Jan 8, 2002||International Business Machines Corporation||Surfaid predictor: web-based system for predicting surfer behavior|
|US6356879||Oct 9, 1998||Mar 12, 2002||International Business Machines Corporation||Content based method for product-peer filtering|
|US6430539||May 6, 1999||Aug 6, 2002||Hnc Software||Predictive modeling of consumer financial behavior|
|US6539377||Oct 6, 2000||Mar 25, 2003||Ask Jeeves, Inc.||Personalized search methods|
|US6567786||Sep 16, 1999||May 20, 2003||International Business Machines Corporation||System and method for increasing the effectiveness of customer contact strategies|
|US6647269||Jul 5, 2001||Nov 11, 2003||Telcontar||Method and system for analyzing advertisements delivered to a mobile unit|
|US6763334||Dec 9, 1999||Jul 13, 2004||Action Click Co., Ltd.||System and method of arranging delivery of advertisements over a network such as the internet|
|US6772129||Sep 4, 2002||Aug 3, 2004||Planning Power Service, Inc.||System and method for determining the effectiveness and efficiency of advertising media|
|US6785421||May 22, 2000||Aug 31, 2004||Eastman Kodak Company||Analyzing images to determine if one or more sets of materials correspond to the analyzed images|
|US6963848 *||Mar 2, 2000||Nov 8, 2005||Amazon.Com, Inc.||Methods and system of obtaining consumer reviews|
|US7007074||Sep 10, 2001||Feb 28, 2006||Yahoo! Inc.||Targeted advertisements using time-dependent key search terms|
|US7031932||Jul 25, 2000||Apr 18, 2006||Aquantive, Inc.||Dynamically optimizing the presentation of advertising messages|
|US7130808||Jun 2, 2000||Oct 31, 2006||The Product Engine, Inc.||Method, algorithm, and computer program for optimizing the performance of messages including advertisements in an interactive measurable medium|
|US7136875||Feb 26, 2003||Nov 14, 2006||Google, Inc.||Serving advertisements based on content|
|US7370002 *||Jun 5, 2002||May 6, 2008||Microsoft Corporation||Modifying advertisement scores based on advertisement response probabilities|
|US7383258||Sep 30, 2003||Jun 3, 2008||Google, Inc.||Method and apparatus for characterizing documents based on clusters of related words|
|US7406434||Dec 17, 2001||Jul 29, 2008||Carl Meyer||System and method for improving the performance of electronic media advertising campaigns through multi-attribute analysis and optimization|
|US7415423||Oct 30, 2006||Aug 19, 2008||Carl Meyer||Method, algorithm, and computer program for optimizing the performance of messages including advertisements in an interactive measurable medium|
|US7818208||Oct 19, 2010||Google Inc.||Accurately estimating advertisement performance|
|US7827060||Dec 30, 2005||Nov 2, 2010||Google Inc.||Using estimated ad qualities for ad filtering, ranking and promotion|
|US20020103698||Dec 1, 2000||Aug 1, 2002||Christian Cantrell||System and method for enabling user control of online advertising campaigns|
|US20020147637||Mar 7, 2001||Oct 10, 2002||International Business Machines Corporation||System and method for dynamically optimizing a banner advertisement to counter competing advertisements|
|US20030023598||Jul 26, 2001||Jan 30, 2003||International Business Machines Corporation||Dynamic composite advertisements for distribution via computer networks|
|US20030032409||Mar 18, 2002||Feb 13, 2003||Hutcheson Stewart Douglas||Method and system for distributing content over a wireless communications system|
|US20030046161||Mar 29, 2002||Mar 6, 2003||Kamangar Salar Arta||Methods and apparatus for ordering advertisements based on performance information and price information|
|US20040054577||Jun 6, 2002||Mar 18, 2004||Toshio Inoue||Advertisement selecting apparatus, advertisement selecting method and storage medium|
|US20040059708||Dec 6, 2002||Mar 25, 2004||Google, Inc.||Methods and apparatus for serving relevant advertisements|
|US20040059712||Jun 2, 2003||Mar 25, 2004||Dean Jeffrey A.||Serving advertisements using information associated with e-mail|
|US20040267723||Jun 30, 2003||Dec 30, 2004||Krishna Bharat||Rendering advertisements with documents having one or more topics using user topic interest information|
|US20050021397||Aug 27, 2003||Jan 27, 2005||Cui Yingwei Claire||Content-targeted advertising using collected user behavior data|
|US20050154717||Mar 22, 2004||Jul 14, 2005||Microsoft Corporation||System and method for optimizing paid listing yield|
|US20050251444||May 10, 2004||Nov 10, 2005||Hal Varian||Facilitating the serving of ads having different treatments and/or characteristics, such as text ads and image ads|
|US20060026071||Oct 4, 2005||Feb 2, 2006||Yahoo! Inc.||Targeted advertisements using time-dependent key search terms|
|US20060173744||Jan 31, 2006||Aug 3, 2006||Kandasamy David R||Method and apparatus for generating, optimizing, and managing granular advertising campaigns|
|US20060288100||May 31, 2006||Dec 21, 2006||Carson Mark A||System and method for managing internet based sponsored search campaigns|
|US20070027756||Apr 28, 2006||Feb 1, 2007||Collins Robert J||Application program interface for optimizing advertiser defined groups of advertisement campaign information|
|US20070067215||Sep 16, 2005||Mar 22, 2007||Sumit Agarwal||Flexible advertising system which allows advertisers with different value propositions to express such value propositions to the advertising system|
|US20070078707||Sep 30, 2005||Apr 5, 2007||Brian Axe||Controlling the serving of advertisements, such as cost per impression advertisements for example, to improve the value of such serves|
|US20070156621||Dec 30, 2005||Jul 5, 2007||Daniel Wright||Using estimated ad qualities for ad filtering, ranking and promotion|
|US20070156887||Dec 30, 2005||Jul 5, 2007||Daniel Wright||Predicting ad quality|
|US20080097834||Dec 21, 2007||Apr 24, 2008||Overture Sevices, Inc.||Method For Optimum Placement Of Advertisements On A Webpage|
|US20100082439||Nov 22, 2002||Apr 1, 2010||Matchcraft, Inc.||Online media exchange|
|US20110015988||Sep 24, 2010||Jan 20, 2011||Google Inc.||Using estimated ad qualities for ad filtering, ranking and promotion|
|JP2002245336A||Title not available|
|JP2002366838A||Title not available|
|JP2003141410A||Title not available|
|JP2005190340A||Title not available|
|JP2005276206A||Title not available|
|JP2007524925A||Title not available|
|KR20010109402A||Title not available|
|KR20020038141A||Title not available|
|KR20020091101A||Title not available|
|WO2001009789A1||Jul 27, 2000||Feb 8, 2001||Tmp Worldwide||Method and apparatus for tracking and analyzing online usage|
|WO2001015053A2||Aug 25, 2000||Mar 1, 2001||Spinway, Inc.||System and method for providing computer network access to a user|
|WO2003023680A1||Sep 10, 2002||Mar 20, 2003||Yahoo Inc.||Targeted advertisements using time-dependent key search terms|
|WO2005006141A2||Jun 30, 2004||Jan 20, 2005||Google, Inc.||Using enhanced ad features to increase competition in online advertising|
|WO2005010702A2||Jul 21, 2004||Feb 3, 2005||Google, Inc.||Improving content-targeted advertising using collected user behavior data|
|WO2005033879A2||Sep 29, 2004||Apr 14, 2005||Google Inc.||Automatically targeting web-based advertisements|
|WO2005043344A2||Nov 3, 2004||May 12, 2005||Google, Inc.||System and method for enabling an advertisement to follow the user to additional web pages|
|WO2005052753A2||Nov 23, 2004||Jun 9, 2005||Google, Inc.||Using concepts for ad targeting|
|WO2005062863A2||Dec 22, 2004||Jul 14, 2005||Google, Inc.||Method and system for providing targeted graphical advertisements|
|1||*||Advertising and Product Quality: are Heavily Advertising Products Better? Herbert J Rotfeld; Kim B Rotzoll. The Journal of Consumer Affairs (pre-1986); Summer 1976; 10, 1; ABI/INFORM Global p. 33.|
|2||Co-pending U.S. Appl. No. 10/878,926; entitled "Systems and Methods for Deriving and Using an Interaction Profile," filed Jun. 28, 2004; Alexis Jane Battle et al; 85 pages.|
|3||Co-pending U.S. Appl. No. 11/167,581; entitled "Accurately Estimating Advertisement Performance," filed Dec. 30, 2005; Eric Veech; 39 pages.|
|4||International Preliminary Report on Patentability dated Jul. 10, 2008 issued in international application No. PCT/US2006/062710 (corresponding U.S. Appl. No. 11/321,046), 8 pages.|
|5||International Preliminary Report on Patentability dated Jul. 10, 2008, issued in International Application No. PCT/US2006/062673 (corresponding U.S. Appl. No. 11/321,064), 8 pages.|
|6||International Search Report and Written Opinion for PCT/US2006/062707, mailed Sep. 24, 2007, (9 pages).|
|7||M. Fang et al.: Computing Iceberg Queries Efficiently, Proceedings of the 24th Very Large Data Bases Conference, 1998, 12 pages.|
|8||Office Action from U.S. Appl. No. 11/167,581, dated Mar. 3, 2009, 16 pages.|
|9||Office Action from U.S. Appl. No. 11/321,046 dated Feb. 19, 2009, 23 pages.|
|10||Office Action from U.S. Appl. No. 11/321,046, dated Sep. 28, 2010, 23 pages.|
|11||Office Action from U.S. Appl. No. 11/321,064 dated Feb. 18, 2009, 26 pages.|
|12||Office Action from U.S. Appl. No. 12/890,271, dated Apr. 1, 2011, 27 pages.|
|13||S. Brin and L. Page: The Anatomy of a Large-Scale Hypertextual Search Engine, 7th International World Wide Web Conference, 1998, 20 pages.|
|14||Shun-Zheng Yu et al., "Dynamic Web pages for location-based services": Jul. 17-19, 2002.|
|15||U.S. Appl. No. 11/321,046; entitled "Predicting Ad Quality," filed Dec. 30, 2005; Daniel Wright et al.|
|16||U.S. Appl. No. 11/321,064; entitled "Using Estimated Ad Qualities for Ad Filtering, Ranking and Promotion," filed Dec. 30, 2005; Daniel Wright et al.|
|Citing Patent||Filing date||Publication date||Applicant||Title|
|US8326845||Dec 4, 2012||Vast.com, Inc.||Predictive conversion systems and methods|
|US8375037||Feb 12, 2013||Vast.com, Inc.||Predictive conversion systems and methods|
|US8438280 *||May 7, 2013||Quantcast Corporation||Detecting and reporting on consumption rate changes|
|US8583483||May 21, 2010||Nov 12, 2013||Microsoft Corporation||Online platform for web advertisement competition|
|US8635519||Aug 26, 2011||Jan 21, 2014||Luminate, Inc.||System and method for sharing content based on positional tagging|
|US8737678||Nov 30, 2011||May 27, 2014||Luminate, Inc.||Platform for providing interactive applications on a digital content platform|
|US8868572||Oct 30, 2012||Oct 21, 2014||Vast.com, Inc.||Predictive conversion systems and methods|
|US9104718||Jun 26, 2013||Aug 11, 2015||Vast.com, Inc.||Systems, methods, and devices for measuring similarity of and generating recommendations for unique items|
|US9158747||Feb 26, 2013||Oct 13, 2015||Yahoo! Inc.||Digital image and content display systems and methods|
|US9324104||Jul 2, 2015||Apr 26, 2016||Vast.com, Inc.||Systems, methods, and devices for measuring similarity of and generating recommendations for unique items|
|US9384408||Jan 12, 2011||Jul 5, 2016||Yahoo! Inc.||Image analysis system and method using image recognition and text search|
|US20100208984 *||Aug 19, 2010||Microsoft Corporation||Evaluating related phrases|
|US20110270686 *||Nov 3, 2011||Microsoft Corporation||Online platform for web advertisement partnerships|
|US20120179544 *||Jan 12, 2011||Jul 12, 2012||Everingham James R||System and Method for Computer-Implemented Advertising Based on Search Query|
|US20120303443 *||May 27, 2011||Nov 29, 2012||Microsoft Corporation||Ad impact testing|
|US20140032301 *||Jul 23, 2013||Jan 30, 2014||Alibaba Group Holding Limited||Advertisement billing method and device|
|USD736224||Oct 10, 2011||Aug 11, 2015||Yahoo! Inc.||Portion of a display screen with a graphical user interface|
|USD737289||Oct 10, 2011||Aug 25, 2015||Yahoo! Inc.||Portion of a display screen with a graphical user interface|
|USD737290||Oct 10, 2011||Aug 25, 2015||Yahoo! Inc.||Portion of a display screen with a graphical user interface|
|USD738391||Oct 11, 2011||Sep 8, 2015||Yahoo! Inc.||Portion of a display screen with a graphical user interface|
|U.S. Classification||705/14.41, 705/14.51|
|International Classification||G05B19/418, G06Q30/00|
|Cooperative Classification||G06Q30/0272, G06Q30/0257, G06Q30/0253, G06Q30/0254, G06Q30/0242, G06Q30/02|
|European Classification||G06Q30/02, G06Q30/0253, G06Q30/0254, G06Q30/0272, G06Q30/0242, G06Q30/0257|
|May 5, 2006||AS||Assignment|
Owner name: GOOGLE INC., CALIFORNIA
Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:WRIGHT, DANIEL;PREGIBON, DARYL;TANG, DIANE;REEL/FRAME:017591/0183;SIGNING DATES FROM 20060410 TO 20060413
Owner name: GOOGLE INC., CALIFORNIA
Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:WRIGHT, DANIEL;PREGIBON, DARYL;TANG, DIANE;SIGNING DATESFROM 20060410 TO 20060413;REEL/FRAME:017591/0183
|Jan 17, 2012||CC||Certificate of correction|
|May 22, 2015||FPAY||Fee payment|
Year of fee payment: 4